Download
NVIDIA NeMo is a scalable, cloud-native generative AI framework aimed at researchers and PyTorch developers working on large language models, multimodal models, and speech AI (ASR and TTS), with growing support for computer vision. It provides collections of domain-specific modules and reference implementations that make it easier to pre-train, fine-tune, and deploy very large models on multi-GPU and multi-node infrastructure. NeMo 2.0 introduces a Python-based configuration system, replacing YAML with more flexible, programmable configs that can be versioned and composed for different experiments. The framework builds on PyTorch Lightning–style modular abstractions, so training scripts are composed from reusable components for data loading, models, optimizers, and schedulers, which simplifies experimentation and adaptation. NeMo is designed to scale: with tools like NeMo-Run, users can orchestrate large-scale experiments across thousands of GPUs.
Features
- Scalable PyTorch-based framework for LLMs, multimodal models, ASR, TTS, and related AI domains
- Python-based configuration system in NeMo 2.0 for flexible, programmatic experiment setup
- Modular architecture built on PyTorch Lightning–style components for models, data, and training loops
- NeMo-Run tooling to scale experiments efficiently across large GPU clusters and heterogeneous environments
- Collections of pre-trained models and reference recipes for speech, language, and multimodal tasks
- Apache 2.0–licensed, cloud-native design that integrates with NVIDIA’s inference and deployment stack for production use
Project Samples
